The field of Retrieval-Augmented Generation (RAG) is moving towards more robust and reliable methods for generating accurate and faithful responses. Recent developments focus on addressing the challenges of complex, multi-hop queries and knowledge-sparse domains. Researchers are exploring innovative architectures and frameworks that incorporate iterative refinement, adaptive query reformulation, and contrastive example generation to improve the performance of RAG systems. Notably, the introduction of agentic designs and modular pipelines is enhancing retrieval robustness in specialized domains. The emphasis is on developing systems that can provide trustworthy and accurate responses, particularly in high-stakes domains such as fintech and religious question answering. Some noteworthy papers in this regard include: FAIR-RAG, which introduces a novel agentic framework for faithful adaptive iterative refinement, and RaCoT, which proposes a plug-and-play contrastive example generation mechanism for enhanced LLM reasoning reliability. FARSIQA is also notable for its application of the FAIR-RAG architecture to the Persian Islamic domain, demonstrating state-of-the-art performance on the IslamicPCQA benchmark.